WPS4127
Poverty and Environmental Impacts of
Electricity Price Reforms in Montenegro
Patricia Silva*, Irina Klytchnikova**, and Dragana Radevic***
Abstract: The Government of Montenegro is preparing an electricity tariff reform due to
recent developments in the national and regional electricity markets. Electricity tariffs for
residential consumers in Montenegro are likely to gradually increase by anywhere from 40 to
over 100 percent. This significant price rise will impose a heavy burden on the poor
households and it may adversely affect the environment. In an ex-ante investigation of the
welfare impact of this price increase on households in Montenegro, we show that the
anticipated price increase will result in a very significant increase in households' energy
expenditures. A simulation of alternative policy measures analyzes the impact of different
tariff levels and structures on the poor and vulnerable households in particular. Higher
electricity prices could also significantly increase the proportion of households using
fuelwood for space heating.
Acknowledgements
We are grateful to the Trust Fund for Environmentally and Socially Sustainable Development
for financial support. We thank Kirk Hamilton, Team Leader, Policy and Economics Team,
ENV, and Ruslan Yemtsov, Senior Economist, ECSPE, for their support. Our thanks also go
to Sushenjit Bandyopadhyay, Husam Beides, Bjorn Hamso, David Kennedy, Priya
Shyamsundar, and staff at the Electricity Company of Montenegro for the many insightful
comments and suggestions.
World Bank Policy Research Working Paper 4127, February 2007
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if
the presentations are less than fully polished. The papers carry the names of the authors and should be cited
accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the
authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries
they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.
*The World Bank, Environment Department (ENV)
**The World Bank, Poverty Reduction and Economic Management Department (ECSPE)
***Center for Entrepreneurship and Economic Development, Montenegro
1. Introduction
Countries with former centrally planned economies in Europe and Central Asia are in the
process of implementing structural reforms in the energy sector. In the past, electricity tariffs
were very low in these countries, inducing inefficient electricity consumption. Furthermore,
electricity tariffs did not cover the operation costs, leading to large subsidies to utility
companies and thus imposing a heavy fiscal burden on the budget. In the 1990s governments
in Europe and Central Asia started to implement market reforms, including restructuring
energy utility companies, liberalizing energy markets, and raising energy prices to the cost
recovery level. The main objective of these reforms was to reduce fiscal subsidies, improve
operational efficiency of the utility companies and raise end-user energy efficiency.
However, the substantial increase in energy prices in the region has had an adverse impact on
poverty and the environment when pricing reforms were not combined with measures to
mitigate the losses from a price increase on the poor households.
Contrary to most other transition economies, where energy intensity declined or remained
stable in the 1990s, in Montenegro energy intensity per unit of GDP increased by as much as
60 percent. This significant increase was a result of low energy prices, a declining GDP level,
and a lack of financing to maintain and upgrade energy infrastructure.1 In order to improve
energy efficiency and the financial situation in the energy sector the Government of
Montenegro began implementing energy sector reforms, which include increases in
electricity tariffs.
Further price increases are now under consideration due to recent developments in the
regional electricity market. Along with the other countries in South East Europe, Montenegro
recently signed a regional energy treaty, which commits the signatory countries to liberalize
the non-residential energy market by January 1st 2008.2 This treaty includes a set of measures
intended to support the development of a regional electricity market, such as raising
electricity tariffs to the cost-recovery level, enforcing payments discipline, restructuring
energy companies, establishing an independent energy regulator, revising tariff
methodologies, and putting in place social safety nets to offset the adverse impact of tariff
increases on vulnerable households. In the next five years these reforms are expected to lead
to a significant electricity price increase in the signatory countries.3 This paper is an ex-ante
analysis of the welfare impact of this price increase on households in Montenegro.
There is some debate about the appropriate cost recovery level that should be used as a
benchmark in setting electricity tariff levels in Montenegro. By some estimates, electricity is
no longer subsidized in Montenegro and the average residential tariff of 4.85 c/kWh,
including taxes, has reached the current short-run cost-recovery price level.4 The long-run
cost recovery level for the region is estimated around 7.0 c/kWh and it includes the
provision for investments in maintenance and upgrading of the infrastructure (Figure 1).5
1 See "Serbia and Montenegro. A Country Environmental Analysis." The World Bank. Washington D.C. (2003).
2 Participating countries in the development of the power market in South East Europe under this treaty include
Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Serbia, Kosovo, the Former Yugoslav Republic of
Macedonia, and Romania. Turkey is also expected to sign the treaty at later date.
3 See Kennedy, David (2006), "World Bank Framework for Development of a Power Market in South East
Europe."
4 Personal communication with David Kennedy (World Bank). Information about the average residential tariff
was provided by EPCG (Elektroprivreda Crne Gore), the Electric Company of Montenegro.
5 In the absence of specific data regarding the cost recovery price level for Montenegro and how much that is
likely to vary given alternative new investment choices, we rely on regional estimates of cost recovery prices
1
However, the cost recovery level in Montenegro could be higher than the regional average,
for example, if the country faces higher generation costs. The electric utility company in
Montenegro (EPCG) is requesting the Regulatory Agency to approve an increase of the
average residential electricity tariff to 10.7 c/kWh and it has argued that an increase of this
magnitude would be necessary to cover the rising labor costs and local communal taxes. This
paper does not aim to resolve the debate of what is the appropriate electricity tariff level and
argue in favor of a particular estimate of the cost-recovery level. Instead, we focus on an
illustration of the anticipated poverty and environmental effects in several policy scenarios.
The Government of Montenegro faces an important policy dilemma. Residential electricity
tariffs could more than double over the next four years if the regulatory agency approves the
price increase requested by the electric utility company. Even a smaller increase in residential
electricity tariffs to 7.0 c/kWh is likely to be extremely unpopular. It could also give rise to
environmental externalities from an increased reliance on fuelwood, the main alternative
household energy source, as households switch away from electricity. However, the cost of
maintaining residential tariffs at the current price level in the face of the rising cost recovery
price, as our analysis shows, would be prohibitively expensive. Maintaining tariffs below cost
recovery levels would also undermine the government's commitment to the development of a
regional electricity market. To resolve this dilemma, the Government of Montenegro could
evaluate a range of alternatives that combine the price increase with measures that would
mitigate the effect of this dramatic price increase on poor and vulnerable households.
In this paper, we use the 2004 ISSP6 household survey data to provide an overview of energy
consumption patterns in Montenegro and examine the likely impacts of electricity tariff
reforms on household welfare. There are legitimate concerns that higher electricity tariffs will
significantly increase the share of energy expenditures of poor households, particularly
during the cold winter months. We also evaluate the distributional and fiscal impacts of
alternative electricity tariff mechanisms using benefit incidence analysis. Our focus is
primarily on the impact of different tariff levels and structures on the poor and vulnerable
households.
Throughout the welfare impact evaluation and benefit incidence analysis we assume that
households do not switch to other heating fuels, but we relax this assumption in the analysis
of household choice of a source of heating. Fuelwood is the main alternative to electricity for
space heating and it is used by more than half of the population either as the only source of
space heating or in conjunction with electricity. Higher electricity tariffs may result in
increased demand for fuelwood, if an increase of electricity tariffs induces a significant
number of households to switch to fuelwood for heating during the winter months. We
investigate the potential impact of electricity tariff reforms on household heating fuel choice
by estimating a fuel switching model. The switch to fuelwood for space heating could
potentially have a negative impact on forest resources, particularly in the North, by inducing
deforestation and on household health by increasing indoor air pollution. The evidence
presented in this paper suggests that the impact of electricity tariff reforms on household
choice of space heating fuels and the level of fuelwood consumption should be carefully
monitored.
reported in Alam, A., M. Murthi, R. Yemtsov, E. Murrugarra, N. Dudwick, E. Hamilton, and E. Tiogson (2005),
"Growth Poverty, and Inequality: Eastern Europe and the Former Soviet Union." Washington, D.C., World
Bank.
6Institute for Strategic Studies and Prognosis (www.isspm.org)
2
Figure 1: Electricity Tariffs in SEE
8.0
7.0
Residential tariffs (c/kWh) ECA benchmark
h 6.0
kW/ 5.0
stneC 4.0
oruE3.0
2.0
1.0
0.0
ainot y
ar avit ani cibl ani ani or aibr
Es nguH La uahtLi dnal airagl aitao R eg aina
Po kavo
Sl pueR balA Bu Cr m
FY doe ent Se
ac
M on Ro
M
EU-8 SEE
Source: World Bank staff estimates, as reported in Alam et al (2005)
2. Overview of energy consumption patterns in Montenegro
How much do the poor spend on energy? In this overview of energy consumption patterns in
Montenegro we focus on winter energy consumption, because space heating accounts for a
substantial share of household energy consumption. Also, we want to be able to examine the
trade offs between the two main sources of heating energy used during the winter months in
Montenegro, which are electricity and fuelwood. Since energy consumption during the winter
months is a necessity, it is not surprising that poor households spend a higher share of total
expenditures on energy. Households in the poorest quintile spend more than twice as much
(12.9% versus 5.2%) of their budget on energy expenditures than households in the highest
quintile of the income distribution. Although many households do not heat their entire living
space, energy consumption in Montenegro is very inefficient. Average energy consumption
per square meter of living space in Montenegro is about 2.5 times greater than in Northern
Europe, where the climate is more severe.7 Higher electricity tariffs would encourage energy
efficiency investments, however, the burden of higher electricity tariffs on the poor and their
choice of heating fuel must also be considered.
Poverty and the choice of space heating fuel. Households in Montenegro use primarily
electricity and fuelwood for heating during the winter months. We find that more than half of
the population (56.6%) uses fuelwood for heating.8 Among the poor the dependence on
fuelwood is even higher, with 86 percent of poor households relying to some extent on
fuelwood for heating. The percentage of households using fuelwood is higher in rural areas
(79.3%) and in the northern part of the country (71.1%), where the availability of fuelwood is
greater and the climate is colder. A more detailed analysis follows, examining the pattern of
7United Nations Development Program (2004), "Stuck in the Past: Energy, Environment and Poverty in Serbia
and Montenegro."
8Forty five percent of the population relies solely on fuelwood to meet their heating needs during the winter
months.
3
energy consumption geographically, by income quintile groups, and across socioeconomic
groups.
Table 1 reports wood and electricity expenditures as a percentage of total household
expenditures for households in different income groups and regions. The regional differences
in household energy consumption patterns are striking. Fuelwood expenditures are
considerably higher in the North (average of 4.9% of household expenditures) and for the
households in the poorest quintile in the North and Central regions (over 5% of household
expenditures). Fuelwood expenditures amount to approximately half of household total
energy expenditures in all income groups in the North. In contrast, households in the South
spend less than 1 percent of household expenditures on fuelwood. The share of fuelwood
expenditures in total energy expenditures is 20 percent or less for households in the South.
Electricity expenditure shares for the poorest quintile group in Montenegro are generally two
to three times higher than the shares for the richest quintile group. A survey of household
electricity expenditures in other eastern European and central Asian countries, shown in
Figure 2, reveals a similar pattern of electricity expenditure shares between low and high
income households. Given that electricity expenditures are a substantial component of the
total budget of the poorest households, an increase in electricity tariffs will likely have a
major impact on their welfare.
Table 1. Energy expenditures as a percentage of household total expenditures
Wood Electricity Total energy
Quintiles North Center South North Center South North Center South ALL
1 5.9 5.5 0.5 6.2 9.2 8.9 12.1 14.7 9.4 12.9
2 5.3 3.9 1.1 4.2 6.0 6.8 9.5 9.9 7.9 9.5
3 4.4 3.3 0.6 3.6 5.7 6.3 8.0 9.0 6.9 8.1
4 3.9 2.1 0.4 3.9 4.6 5.4 7.8 6.7 5.8 6.9
5 2.8 1.1 1.0 3.6 3.7 4.1 6.4 4.8 5.1 5.2
ALL 4.9 3.8 0.7 4.7 5.6 6.0 9.6 6.0 9.6 8.8
Source: calculated from 2004 ISSP Montenegro Household Survey.
4
Figure 2: Electricity Expenditure Shares of Poorest
and Richest Households in ECA
10%
8%
6%
4%
2%
0%
n
ain ain na s
rua ia aig nd aib y nei
lbaA ar or
rmeA yragn sta yz a
kh negro ania ra
erbaij Bel Ge za Kyrg nte Pola m ssiauR Ser jikistan Turke Uk
Az Bulg Hu Ka Moldov
Mo Ro Ta
Poorest Richest
20% 20%
Source: World Bank staff estimates, as reported in Alam et al (2005)
For some households that use only electricity as a source of heating, the impact of higher
electricity tariffs may be particularly severe. As shown in Table 2, the share of electricity
expenditures in total household budget falls across all social and economic categories as
income rises. In all but three instances, the share of electricity expenditures for households in
the lowest income quintile group exceeds 10 percent of household expenditures. Particularly
vulnerable are poor households with disabled persons and on family material assistance, with
electricity expenditures reaching 13 percent of household expenditures, and households
headed by an unemployed or retired person, which have electricity expenditures shares four
times higher than similar household in the highest quintile group. Thus, at current prices
electricity expenditures already exceed what is considered the benchmark affordability level
of 10 percent of household expenditures.9 The next section examines how higher electricity
tariffs would affect household welfare and electricity expenditure shares, particularly the
burden of higher electricity tariffs for the poor and vulnerable households.
9See "Electricity Poverty and Energy Poverty," IPA report (October 2003).
5
Table 2. Electricity expenditures share for households using electricity only for heating
Gender of
Household Education of Household Head Employment of the Household
Head Head
No school Some
Quintile Male Female or primary secondary Secondary Un- Employed Retired
completed school + employed
1 11.2 11.4 11.0 11.2 11.7 11.9 10.1 11.4
2 7.3 7.4 7.3 7.4 7.1 8.0 6.9 6.9
3 6.9 5.4 6.5 6.7 6.7 3.0 6.3 7.1
4 5.4 8.4 5.3 5.4 5.9 3.9 5.5 5.5
5 4.2 4.5 3.2 4.3 4.3 3.1 4.3 3.8
ALL 6.3 6.9 6.3 6.5 6.1 8.2 5.7 6.9
Kids in the Social
family Disabled in family Assistance Roma Dwelling
No
Kids kids No Disabled No Non
Quintile up to 5 or disabled in family FMS FMS Roma Roma House Apt
older
1 9.3 11.6 10.8 13 12.9 9.8 6.9 11.3 9.9 11.8
2 8.1 7.0 7.4 6.7 7.6 7.2 n/o 7.3 7 7.4
3 8.7 6.3 6.4 9.5 8 6.5 5.5 6.7 6.8 6.6
4 5.4 5.6 5.5 6.3 4 5.6 4.4 5.6 5.8 5.4
5 4.4 4.2 4.2 3.7 n/o 4.2 n/o 4.2 4.5 3.9
ALL 6.6 6.3 6.1 8.2 9.8 6.2 4.7 6.4 6.2 6.5
Source: calculated from 2004 ISSP Montenegro Household Survey.
6
3. The impact of electricity tariff reform on household welfare
In most scenarios of the proposed electricity tariff reforms that are currently under
consideration the price of electricity would increase substantially. How hard will it be
for households to adjust their consumption of electricity if they face higher electricity
prices? Ideally we would want to have data on household consumption of electricity at
different price levels to estimate the demand for electricity. Then we could derive the
price elasticity of electricity demand, which would indicate how responsive electricity
consumption would be to the proposed electricity tariff reforms. Unfortunately, data on
Montenegrin household electricity consumption at different electricity price levels are
not available in the ISSP 2004 survey or from previous household surveys.
Estimating the welfare loss associated with higher electricity tariffs.10 In the absence
of electricity price variation to estimate a household demand function for electricity we
calculate the welfare loss making different elasticity assumptions of household
response to the proposed increase electricity price tariffs. The welfare loss is calculated
as the loss in consumer surplus from the price increase, expressed as a percentage of
total household expenditures. We assume three different electricity price elasticities. At
one extreme, we assume that the price elasticity of electricity demand is zero and
households do not change the quantity of electricity they consume when they face
higher prices. This is our "worst case" scenario, in which household losses are the most
significant. At the other extreme, we assume an elasticity of -1, which indicates that
households' response to electricity price changes is proportional to the magnitude of the
price change. In this scenario, a 10 percent increase in electricity prices generates a 10
percent reduction in electricity consumption. A more realistic case is an intermediate
scenario, which assumes an elasticity of -0.5. In this scenario household demand is
inelastic but it is responsive to higher electricity prices.
Household welfare losses in different price elasticity scenarios. Table 3 presents the
results of the analysis of welfare losses associated with higher electricity prices. The
analysis suggests the welfare losses of the poorest quintile are approximately twice as
large as the welfare losses of the richest quintile. The welfare losses for the poorest
households are larger because the poor spend a higher share of expenditures on energy
than the non-poor. Assuming the initial reforms result in an increase of the price of
electricity to 7 c/kWh, the welfare loss of the poorest households would range from
2.66 to 3.41 percent of household expenditures. However, if households use only
electricity for heating (and assuming they cannot switch to fuelwood), the welfare
losses of the poorest households would range from 3.86 to 4.96 percent of household
expenditures. The welfare losses are of course greater for higher electricity prices. The
welfare losses assuming a zero elasticity are proportional to the price change, i.e. the
welfare loss associated with a 50 percent increase in price would be twice as large as
the welfare loss associated with a 25 percent increase in price. When demand is elastic,
however, the rise in welfare losses is less than proportional to the price increase.11
10We follow the approach used by Caroline Freund and Christine Wallich (1997) in" Public-Sector Price
Reforms in Transition Economies: Who Gains? Who Loses? The Case of Household Energy Prices in
Poland." Economic Development and Cultural Change.
11The welfare losses estimated assume households do not make investments to improve energy
efficiency and are thus best interpreted as short term welfare losses. Clearly, higher electricity prices
7
Table 3. Welfare losses associated with electricity tariff increases1
Electricity price increase to 7 c/kWh
All Households Households using electricity
only for heating
Elasticity 0 -0.5 -1 0 -0.5 -1
1 (Poorest) 3.41 3.04 2.66 4.96 4.41 3.86
2 2.35 2.09 1.83 3.24 2.88 2.52
3 2.31 2.05 1.79 2.97 2.64 2.31
4 1.99 1.77 1.55 2.48 2.21 1.93
5 (Richest) 1.68 1.50 1.31 1.86 1.66 1.45
ALL 2.39 2.13 1.86 2.84 2.52 2.21
Notes: Welfare losses expressed as a percentage of household expenditures.
1
Calculations based on data from the 2004 ISSP Montenegro Household Survey.
Comparing the welfare losses of the poor across different socioeconomic groups. We
are particularly interested in the welfare losses incurred by the poorest households.
Table 4 presents the estimated welfare losses for households in the lowest income
quintile, categorized according to different socioeconomic characteristics. We focus on
households using only electricity for heating. In this scenario it is interesting to
compare the current budget share spent on electricity and the welfare losses expressed
as percentage of total household expenditures. The sum of the budget share of
expenditures and the associated welfare loss when the price elasticity is zero represents
the total electricity expenditures for the poor at the new electricity prices, assuming that
households do not switch to other energy sources for heating. This is a reasonable
approximation if we believe that the household demand function is fairly inelastic,
since households are likely to be consuming the minimum amount of energy necessary
to withstand the cold winter temperatures. If households are already at their minimum
electricity consumption level and they have no possibility to switch to other heating
fuels, then the impact of higher electricity prices on household budgets would indeed be
severe. Some poor households, such as those with disabled household members or
receiving family material assistance, are spending 13 percent of their budget on
electricity. Assuming a zero price elasticity, they would be spending nearly 20 percent
of their budget on electricity if prices increase to 7 c/kWh.
If we assume households do respond to higher electricity prices by reducing
consumption of electricity, we cannot add the total welfare losses to the share of
electricity expenditures to obtain the new share of electricity expenditures. This is
because when the elasticity is not zero, the welfare losses include additional
expenditures on electricity, as well as the consumer surplus loss incurred on the value
of units not consumed at the higher price. Thus, when we calculate the welfare losses
for an elasticity of -0.5, we present the consumer surplus loss and financial loss (or
would induce households to invest in more energy efficient electrical appliances and better insulation in
their homes in the long run. After such adjustments households would be able the same level of comfort
using less energy.
8
additional expenditures) separately. As Table 4 shows, the consumer surplus loss
outweighs the financial loss incurred as a result of higher electricity tariffs. The burden
of higher electricity tariffs on household expenditures is lower when households reduce
their consumption of electricity. Previous studies have estimated price elasticity of
demand for heating to fall somewhere between -0.2 and -0.4, with poorer households
being in the more inelastic range of these estimates.12 Therefore our calculations of
welfare losses for zero and -0.5 elasticity assumptions fall within the range of
reasonable elasticity estimates.
Table 4. Welfare losses of poorest households in different socioeconomic groups1
Share of
electricity in Elasticity 0 Elasticity -0.5
household Consumer Financial
budget Welfare loss surplus loss loss
Gender of Male 11.2 5.0 3.0 1.4
Household Head Female 11.4 5.1 3.1 1.4
No school or primary
Education of completed 11.0 4.9 3.0 1.4
Household Head Some secondary
school 11.2 5.0 3.0 1.4
Secondary + 11.7 5.2 3.2 1.4
Household head Unemployed 11.9 5.3 3.2 1.5
employment Employed 10.1 4.5 2.7 1.2
Retired 11.4 5.1 3.1 1.4
Kids in the Kids up to 5 9.3 4.1 2.5 1.1
family No kids or older 11.6 5.1 3.1 1.4
Disabled in No disabled 10.8 4.8 2.9 1.3
family Disabled in family 13 5.8 3.5 1.6
Social Assistance FMS 12.9 5.7 3.5 1.6
No FMS 9.8 4.3 2.7 1.2
9.9 4.4 1.4
Dwelling House 2.7
Apartment 11.8 5.2 3.2 1.4
Notes: 1 Welfare losses expressed as a percent of household expenditures and calculated assuming
electricity price increase to 7 c/kWh using the 2004 ISSP Montenegro Household Survey.
4. Comparison of social assistance options in the course of reforms
Residential electricity consumption is not currently subsidized in Montenegro, since the
average residential tariff of 4.85 c/kWh, inclusive of the taxes, is approximately equal
to the cost-recovery price. However, this situation will change in the future, and
therefore the Government of Montenegro is seeking to design an effective social safety
net program to mitigate the effect of a tariff increase on the poor. This section explores
the advantages and disadvantages of alternative social assistance options in the course
of reforms in the electricity sector in Montenegro.
Provision of utility subsidies is a common way of mitigating the effect of electricity
tariff increases on the poor, particularly wide-spread in developing and transition
12Lampietti, J., A. Meyer (2002), "Coping with the Cold: Heating Strategies for Eastern Europe and
Central Asia's Urban Poor." Washington, D.C.: World Bank.
9
countries. Electricity subsidies can be financed through a direct transfer of government
funds or through a cross-subsidy between different groups of consumers. In this paper
we refer to both types of transfer, whether from the government or from other
consumers, as a subsidy. It is common to provide subsidies directly through the energy
system by designing an Increasing Block Tariff (IBT), also called the "lifeline tariff,"
or through a Volume Differentiated Tariff (VDT).13 Another approach is to provide
means-tested income transfers to qualifying households through a general social
assistance program. "Social tariffs" combine the elements of these two approaches (see
Table 5). For example, an IBT or other types of electricity discounts could be provided
only to the qualifying customers, identified by administrative selection.
Table 5. Comparison of mitigation options of the effect of a tariff increase on the poor
Program type Description Coverage Targeting
performance performance
Increasing Block Tariff A lower price is charged per kWh up Depends on the Depends on the
(IBT) (same as the to a pre-determined consumption threshold. Error of threshold. Error of
"lifeline tariff") threshold (lifeline limit). exclusion of the poor inclusion of the
Consumption above the threshold is low if the threshold non-poor is high if
limit is charged a higher price per is high. the threshold is
kWh. All households consuming high.
electricity benefit from the lower
tariff below the threshold.
Volume Differentiated A lower price is charged per kWh up Depends on the Depends on the
Tariff (VDT) to a pre-determined consumption threshold. Error of threshold. Error of
threshold only if monthly electricity exclusion is likely inclusion is lower
consumption is below the threshold. higher than with a than with a
If consumption is above the comparable IBT as comparable IBT.
threshold, a higher price per kWh many poor households
applies to all kWh consumed. Only may exceed the
households consuming less than the threshold level.
threshold level benefit from the
subsidy.
"Social tariffs" IBT, VDT, or discounts provided to If the social protection system is effective at
qualifying households, identified as identifying the poor, means-tested transfers
poor. or targeted "social tariffs" are superior to
Means-tested social Qualifying households, identified as IBT and VDT in terms of coverage and
assistance transfers poor. targeting.
Note: this table is based on the discussion in Komives et. al. (2005).
Other approaches are burden limit, earmarked cash transfers, across-the-board subsidy
and no disconnection policy. The burden limit approach means that the actual
household utility expenditures are capped at a specified level, for example 10 percent of
total household expenditures. An example earmarked cash transfers is the provision of
cash transfers that can only be used for paying the utility bills. A subsidy could also be
provided to all electricity consumers across-the-board when the tariff level is linear and
it is set below the cost recovery level. Last, an implicit subsidy could be provided to
consumers with poor payment discipline through a no disconnection policy. In
13Komives, K., V. Foster, J. Halpern, and Q. Wodon (2005), Water, Electricity, and the Poor. Who
Benefits from Utility Subsidies? Washington, D.C.: World Bank.
10
Montenegro payment arrears are relative low and the collection rate is close to 90
percent14.
Each approach has advantages and disadvantages according to a range of criteria. They
are coverage, targeting, predictability of a subsidy, the pricing distortion it creates,
administrative costs and the ease of implementation. As shown in Appendix Table 1,
there no approach that is unequivocally preferable to all other ways of providing utility
subsidies. Policy makers face a trade-off between the coverage and targeting
performance of electricity subsidies. Coverage is measured by the share of subsidy
recipients who are among the poor. A high percentage of beneficiaries among the poor
indicates that a program is effective at reaching them. Coverage is high and the error of
exclusion of the poor is low in this case. Targeting is measured by the share of the total
subsidy going to the poor. A high share indicates that the resources are not leaking to
the non-poor, and the error of inclusion of the non-poor among the recipients is low. As
shown in Table 5, there is a trade-off between coverage and targeting.
The administrative ease with which a particular tariff scheme could be implemented
and predictability of a subsidy should be an important factor in the evaluation of a
feasibility of implementing a particular tariff scheme. Non-linear pricing schemes could
pose administrative challenges. Although Serbia has implemented an IBT system, the
experience in other countries is mixed. Often billing systems cannot handle it without
major re-design. Meter readers may have to arrive on exactly the same day of the
month at a given consumer, or there could be endless disputes about the monthly
consumption vs. the thresholds. Particularly in the case of VDT this can be a
prohibitive issue and cause massive corruption.
There is no one-size-fits-all solution to designing an effective mitigation strategy in the
course of energy pricing reforms. The general advice of the World Bank and the IMF is
to use means-tested subsidies to soften the negative impact of rising utility tariffs on
low income households, as this approach does not give rise to price distortions.
However, administering a well-targeted means-tested program that delivers direct
income transfers to the poor is fraught with implementation problems due to the
difficulty in identifying eligible households, especially if households do not accurately
report their income for taxation purposes.15 Thus, implementation issues must be
carefully evaluated in the design of a safety net system for the poor.
The targeting and coverage performance of a subsidy depends on the pattern of
electricity consumption by different income groups. A systematic review of electricity
consumption subsidies in 22 countries reached a conclusion that an IBT, the most
common tariff approach to providing subsidies, tends to have high coverage of the poor
14According to EPCG, if old debts are excluded, the collection ratio varied between 62.4 and 89.4
percent between 1999 and 2004, and in 2004 it varied between 69 and 100 percent by region. It is
important to evaluate the relationship between tariff increases and arrears to see if high tariffs lead to
deterioration in the payment discipline. Such data are not available in the survey data we use in this
paper, so we are not able to investigate this relationship.
15For an in-depth discussion of the ways to design a successful means-tested direct income transfers
program see Coady, Grosh and Hoddinot (2004). Targeting Transfers in Developing Countries. Review
of Lessons and Experience. The World Bank. Washington, D.C.
11
but be poorly targeted. Modifying the block structure of an IBT can result in small
improvements in terms of better targeting of the subsidy to the poor households.
Introducing a VDT or well-targeted income transfers through the social protection
system rather than the electricity sector can result in significant improvements.16
While it is usually true that an IBT does not result in well-targeted subsidies, in
Montenegro electricity consumption is highly correlated with income. Households in
the top decile of total per capita expenditures consume almost three times as much
electricity as households in the bottom decile (Appendix Table 2). Although per capita
consumption of electricity is almost constant with respect to income, the average
household size is almost twice as large in the top as in the bottom decile. Figure 3
clearly shows the strong correlation between total household electricity consumption
and household size. As a consequence of this strong correlation, an IBT or a VDT will
be better targeted in Montenegro than in most other countries.
Figure 3: Electricity consumption and
household size in Montenegro
dlohe 1200 5
1000
us 4
ho 800 zesi
3 d kWh/month
per 600 hol
Household size
nth 2 use
400 ho
h/mo 200 1
Wk 0 0
1 2 3 4 5 6 7 8 9 10
deciles of total PCE
Source: Calculated from 2004 ISSP Montenegro Household Survey.
Benefit incidence analysis of alternative electricity tariff scenarios in Montenegro. In
the remainder of this section we evaluate the distributional and fiscal impact of
alternative electricity tariff reform scenarios by conducting benefit incidence analysis
of in-kind electricity subsidies that households in Montenegro would receive in five
policy scenarios. The analysis includes three steps. First, we calculate the subsidy per
kWh as the difference between the cost-recovery price, assumed here to be 7 c/kWh,
and the electricity tariff households are charged. Second, we estimate the total annual
subsidy received by each household as the average level of electricity consumption
multiplied by the subsidy per unit. Third, we calculate the total subsidy received by the
poor and non-poor households. Last, we compare the coverage and targeting
16Komives et. al. (2005).
12
performance of the simulated policy options and the financing requirements. The
assumptions of the policy scenarios considered here are summarized in Table 6. They
are not based on the actual reform proposals of the Government or the regulatory
agency, as the Energy Law in Montenegro does not allow subsidizing electricity for
particular groups of consumers using non-linear pricing.17 We include these scenarios
for illustrative purposes in order to show how well alternative pricing schemes would
fare in Montenegro.
Table 6. Scenarios of electricity pricing policy reforms1
Scenario Description Average Lower Upper Threshold Unfunded
tariff tariff tariff subsidy2
1 "Business as Usual Scenario" of 4.85 32.6
no price increase while the cost- million
recovery price rises Euro/year
2 Increasing Block Tariff (IBT) 4.85 7.0 300 14.1
million
Euro/year
3 Increasing Block Tariff (IBT) 4.85 8.6 300 None
4 Volume Differentiated Tariff 4.85 7.5 500 None
(VDT)
5 A linear tariff increase to 7.0 2.8 million
7c/kWh combined with a Euro/year
Targeted income transfer of 10 (cost of
Euro/month to current FMS the FMS
recipient households transfer)
Notes: We assume that the current average tariff is 4.85 c/kWh (including taxes), and it is equal to the
1
cost-recovery tariff. The future cost-recovery tariff in these simulations is assumed to equal 7 c/kWh.2
In Scenarios 1 and 2 we calculated from the household survey data that this is the amount that would
need to be financed either by the government or the energy company in scenarios where it is not covered
by a cross-subsidy from high volume to low volume consumers.
What is the fiscal cost of each policy option? In the "Business as Usual Scenario"
(Scenario 1) we assume that the cost-recovery price rises to 7.0 c/kWh, but the average
electricity tariff remains at the current level of 4.85 c/kWh. If this subsidy were funded by
the government, the fiscal cost of the electricity subsidy to the household sector in this
case would be approximately 32.6 million Euros per year. The size of the subsidy
would be so large that it would be comparable to the entire social protection budget of
Montenegro or its 2004 budget deficit, and this is clearly not be a feasible policy
option.18
In Scenario 2 all consumers pay 4.85 c/kWh for the first 300 kWh they consume, and
they pay the cost-recovery price (7.0 c/kWh) for what they consume above this
threshold. The fiscal cost of this subsidy would be 14.1 million Euros per year, since in
this case only the first 300 kWh of electricity consumption is subsidized. If the
17Personal comm. with the Ministry of Economy and EPCG (February 2006).
18
The budget of the Ministry of Labor and Social Welfare is about 43 million Euros or about 3% of GDP,
out of which about 90% or 38.7 million Euros is allocated to social benefits. Budget deficit in MN in
2004 was about 32 million Euro or 2.1% of GDP.
13
Government of Montenegro wished to establish a self financing mechanism instead, it
would be necessary to raise the price on consumption above the 300 kWh threshold to
8.6 c/kWh, a very steep price increase (Scenario 3). High-volume consumers would thus
cross-subsidize low-volume consumers in this scenario. It may or may not be politically
feasible to implement such a significant price increase. In Scenario 4 we show that a
VDT with a higher threshold of 500 kWh and a lower tariff on consumption above the
threshold than in Scenario 3 would also be fiscally neutral and could be a viable
alternative to the IBT.
How large is the subsidy and what share of it goes to the poor? The size of the
subsidy varies by scenario. In Scenario 1 high volume consumers, who are also the
highest income consumers, receive the largest monthly subsidy (Figure 4 and Appendix
Table 3). In Scenario 2 the subsidy is only for the first 300 kWh, and it is the same for
all income groups since on average all households consume more than 300 kWh per
month. Interestingly, in Scenario 2 the share of the total subsidy going to the poorest
households is much higher than the share going to the highest income deciles. In this
scenario, households in the bottom two deciles receive 27 percent of the total subsidy,
while households in the top two deciles receive 17 percent (Appendix Table 4). Even
though the subsidy per household is the same in absolute terms, there are twice as many
households in the bottom decile than in the top decile, because poor households are half
as large as the richest ones. Since the deciles are based on population and the poor
households are small, the bottom decile has more households than the other deciles.
Thus, it is important to look not only at the share of the total subsidy going to the
poorest households, which is the commonly used targeting indicator, but also at the
absolute level of the subsidy per household while evaluating the welfare impact of
alternative tariff structures in Montenegro.
The average subsidies per household in Scenarios 3 and 4 are similar. As shown in
Figure 4, they are in the range of 2 to 4 Euros for households in the bottom three
deciles, while high income households pay a higher average tariff than the cost-
recovery level on their total electricity consumption (Appendix Table 2). In these
scenarios high income households cross-subsidize the poor, and both of these scenarios
are fiscally neutral. Scenario 3 has the disadvantage of a very high tariff on electricity
consumption above the 300 kWh threshold, but it has broad coverage, with 100 percent
of the poor receiving the subsidy. Scenario 4 has a lower tariff on electricity
consumption above the 500 kWh threshold and a slightly higher average subsidy to the
poor households. However, it has low coverage and omits a significant share of the
poor.
14
Figure 4: Average monthly electricity subsidy
by household income deciles
ht 30
25
monrep 20
dol Scenario 1
15
eh Scenario 2
us 10 Scenario 3
ho 5 Scenario 4
per Scenario 5
0
rouE 1 2 3 4 5 6 7 8 9 10
-5
-10
Source: calculated from 2004 ISSP Montenegro Household Survey.
What share of the poor receives a subsidy? The five alternative subsidy programs we
have considered in this analysis are very different in terms of coverage performance.
Clearly, all households are subsidy recipients in Scenario 1 when everybody is charged
a tariff below the cost-recovery level (Table 7). In Scenario 2 the price of the first block
is below the cost-recovery level, and at the cost-recovery level for the second block. In
this case all consumers are subsidy beneficiaries. In Scenario 3 all consumers receive a
subsidy for the first 300 kWh; however, high volume consumers pay a higher price than
the cost-recovery level for consumption in excess of 300 kWh. Since non-poor
households are high volume consumers, our calculations show that overall they would
be cross-subsidizing the poor. This result is unique to the countries like Montenegro,
where higher income households consume significantly more electricity than low
income households, making it relatively easy to implement quantity-based targeting
with an IBT structure.19 However, it is necessary to have a very large difference
between the price for consumption below and above the threshold, and it may not
necessarily be politically feasible.
A VDT in Scenario 4 would require a smaller difference between the lower and upper
consumption blocks in order to remain fiscally neutral. However, this tariff scheme
would perform worse in terms of coverage. As shown in Table 7, only two thirds of the
poorest households would receive any subsidy. The remaining 26 percent of
households in the bottom decile consume more than 500 kWh and they would pay for
their consumption at the higher rate, assuming that they do not adjust their consumption
level in response to the change in tariff policy.
19It is common for electricity consumption to increase with income, but, as shown in Komives et. al.
(2005), it is less common to see such a high discrepancy in the consumption level of the connected poor
and the non-poor.
15
Targeted transfers in Scenario 5, which assumes that current FMS recipients qualify for
an increased transfer to offset higher electricity tariffs, would reach a low share of the
poor. The Government of Montenegro would need to develop a well-targeted and
broad-based program of social assistance if benefit provision through direct income
transfers were to be the chosen mitigation option. However, this would be an expensive
program and the Government does not currently planning to re-design the social
protection system in order to broaden the recipient base and improve benefit targeting.
Table 7. Subsidy recipients (in percent of total households in the income category)
deciles of total per capita household expenditures
1 2 3 4 5 6 7 8 9 10 Total
Scenario 1 100 100 100 100 100 100 100 100 100 100 100
Scenario 2 100 100 100 100 100 100 100 100 100 100 100
Scenario 3 100 100 100 100 100 - - - - - 57
Scenario 4 74 54 47 32 31 - - - - - 29
Scenario 5 22 19 10 11 6 9 10 16 7 4 12
Total number of
households (`000) 29 25 17 18 18 18 16 16 15 16 187
Source: calculated from 2004 ISSP Montenegro Household Survey.
Limitations of benefit incidence analysis. In this section we have conducted a static
analysis, assuming that households do not adjust their consumption of electricity to the
price increase. In reality, some households will adjust by reducing their electricity
consumption and switching away from electricity to fuelwood. To the extent that the
poor switch at a higher rate than the non-poor, the distribution of the beneficiaries of a
subsidy can change in the policy scenarios examined. This is especially true for the
VDT scenario. The poorest households whose consumption is close to the 500 kWh
threshold are likely to reduce their consumption below the limit so that they would
qualify for the lower tariff, especially if some mechanism is in place to prevent them
from accidentally exceeding the limit and switching to the higher tariff.
The benefit incidence analysis in this section is informative for comparing the efficacy
of alternative tariff structures at reaching the poor in the process of electricity tariff
reforms. However, it is also important to recognize the limitations of this approach. The
distribution of a subsidy does not imply anything about the distribution of the welfare
impact of different programs. For example, poor households with low consumption of
electricity may have a higher valuation of each additional unit of consumption and it
may be more difficult for them to substitute away from electricity than it is for
households with a high consumption volume. By the same token, households with no
access to substitute fuels or facing technical constraints in switching fuels would also
incur higher welfare losses from an electricity tariff increase than households with easy
access to substitute fuels. Furthermore, switching to fuelwood gives rise to externalities
from an increased rate of deforestation. Benefit incidence analysis is not intended to
address any of these issues. This type of analysis is useful for comparing the
distribution of the subsidy via different mechanisms, but not to determine the socially
optimal level of a subsidy.
16
5. The potential impact of energy price reform on household fuel choice
The analysis of electricity price reforms so far has assumed that household choice of
heating fuel does not change. However, evidence from other countries in the region
suggests electricity tariff reforms can have a significant impact on household fuel
choice. For example, in Armenia, more than 60 percent of households reported
increasing use of fuelwood as a substitute when the price of electricity increased by
about 50 percent.20 The magnitude of proposed electricity price reforms in Montenegro
therefore warrants at careful look at the potential impact of higher electricity prices on
fuel substitution.
Approach to estimating household fuel choice.21 We estimate a multinomial logit
model to investigate what factors determine household choice of a heating fuel. Since
most households in Montenegro use electricity or fuelwood for heating, we specify a
multinomial logit model with three choices: use only electricity for heating, use only
fuelwood for heating, and use a mix of both electricity and fuelwood for heating.22
Other types of heating fuel, such as gas, fuel oil, and diesel, are rather rare in
Montenegro. A small percentage of households in the North use coal for heating as
well, generally in conjunction with electricity and/or fuelwood. However, since the
number of households using coal for heating amount to less than 5 percent of the
sample, we do not include coal as a heating fuel choice in the analysis. We model
household choice of heating fuel as a function of prices, income, household social and
economic characteristics, housing characteristics, and location where the household
lives.
Prices are clearly an important determinant of household heating fuel choice. Although
the price of electricity does not vary across households, the price of fuelwood varies
considerably across the country. Since the price of electricity does not vary, we cannot
directly estimate its impact on heating fuel choice. We therefore examine the impact of
the price of fuelwood relative to the price electricity.23 Household per capita
expenditures are used instead of income, due to a large number of missing observations
on reported income. The importance of income/expenditures in household fuel choice is
well documented.24 As income rises, household consumption of biomass fuels
decreases and the uptake of modern fuels increases. However, one may be concerned
that the inclusion of income and household characteristics which may partly determine
20Lampietti, Julian, Anthony Kolb, Sumila Gulyani, and Vahram Avenesyan (2001). Utility Pricing and
the Poor: Lessons from Armenia. World Bank Technical Paper No. 497.
21The model used here follows Rasmus Heltberg's analysis in Fuel Switching: Evidence from Eight
Developing Countries. Energy Economics (2004).
22During the winter, households use fuelwood for heating as well as for cooking. This is simply because
they typically have only one cooker in the house, mainly in the living and dining rooms (very often,
those two rooms are connected).
23We define the relative price as the ratio of the price of fuel wood to the price of electricity. The price
of fuel wood is converted into kWh units, using the standard energy conversion factor of 2,610 kWh in
one cubic meter of fuel wood. This assumes moisture content is one third of weight. The efficiency of
using fuel wood is clearly determined by the type of wood stove used and the effective energy content of
one cubic meter of fuel wood will be different for households using different types of wood stoves.
24See Barnes, D.F., Krutilla, K., Hyde, W. (2004), The Urban Energy Transition? Energy, Poverty, and
the Environment.
17
income levels may lead to bias estimates. We therefore compare the results of the
model estimated with and without the household per capita expenditures variable. We
find that the inclusion of household per capita expenditures does not significantly affect
any of the other explanatory variables coefficients, and report the results including per
capita expenditures as one of the explanatory variables (as do most other energy
studies).
Household fuel choices are influenced by the household's social and economic
characteristics. When households produce or collect the fuelwood they use, their use
and collection is influenced by the opportunity costs and productivity of household
members' labor. Thus, households that own their wood plot would be more likely to
use fuelwood, as would larger households, particular those with unemployed or
underemployed members. Households with higher education, on the other hand, would
be less likely to use fuelwood. We include in the model the following household
characteristics: household size, the number of children under 5 years of age, education
level of household head, gender of household head, employment status of household
head, whether the household owns a wood plot, whether the household receives family
material assistance, and whether the household belongs to the Roma population.
Housing characteristics are clearly important and may determine to what extent
household are able to choose fuelwood as an alternative. Households living in
apartments rather than houses may be more constrained in their choice of heating fuel.
Newer housing, particularly apartments, may not be fitted with proper ventilation to
allow burning of wood indoors. Finally, if the dwelling is renter occupied, it is less
likely that the tenant would invest in modifications to allow use of fuelwood as an
alternative. We thus include in the analysis housing characteristics such as: dwelling
type (house or apartment), whether the household owns the dwelling, age of the
dwelling, and the size of the living area. Regional dummies are used to control for
climate differences and other unaccounted regional differences.
Main results of the household fuel choice model. The results of the multinomial logit
model are reported in Table 8. We select households that use only electricity as the base
case scenario and report the variables which affect full or partial use of fuelwood for
space heating. Price and income are significant determinants of a using only fuelwood,
but not of a partial use of fuelwood. Higher relative prices decrease the probability of
using only fuelwood for space heating. This means as electricity prices increase, the
relative price of fuelwood falls and therefore the probability of using only fuelwood for
space heating increases. Richer households are less likely to use only fuelwood for
heating.
Household living in a house are significantly more likely to use fuelwood for space
heating, as are households living in the North. Household head education and gender
also affect fuel choice. More educated household heads are less likely to use only
fuelwood, whereas households headed by females are more likely to use only fuelwood
for space heating. Larger household are also more likely to use fuelwood than smaller
households. Ownership of a wood plot also significantly increases the probability of
using fuelwood for space heating. Thus household characteristics matter a great deal in
the choice of heating fuel. Larger household size, which could indicate the availability
18
of free household labor to chop and transport fuelwood, and ownership of a wood plot,
may make fuelwood an even cheaper alternative heating fuel. Female headed
households and households headed by a less educated individual are more likely to use
fuelwood, even when household income is controlled for.
Table 8. Multinomial Logit Regression Results1
Log likelihood = -494.84 Pseudo R2 = 0.377
LR chi2(36) =599.03
Prob > chi2 = 0.000 Number of observations = 840
Fuel Choice2
Fuelwood only Fuelwood and electricity
Variables Coefficients Std. Error Coefficients Std. Error
Relative price (Pwood/Pelectricity) -1.671*** 0.434 -0.514 0.505
Per capita expenditures -0.007*** 0.002 -0.001 0.002
Household size 0.206** 0.103 0.389*** 0.120
No. children under 5 -0.008 0.221 -0.093 0.274
Household head education -0.117*** 0.031 -0.067* 0.036
Female household head 0.616** 0.310 0.386 0.389
Household head unemployed 0.087 0.333 -0.832 0.445
Household head retired 0.252 0.283 0.174 0.318
Family receives material
assistance 0.001 0.346 0.454 0.441
Roma 1.130 0.735 -0.495 1.311
Live in a house 2.465*** 0.283 2.136*** 0.330
Own house 0.186 0.319 0.164 0.425
Age of house -0.002 0.004 0.005 0.003
Living space (m2) 0.000 0.004 0.005 0.004
Household owns wood plot 1.237** 0.542 0.096 0.656
North region 3.431*** 0.425 1.842*** 0.466
Central region 1.338*** 0.340 0.494 0.358
Constant 2.506 1.192 -2.651 1.408
1The analysis focuses on choice of heating fuel, not fuel for all energy consumption. Heating constitutes the
most significant share of winter energy expenditures.
2Electricity only is the base outcome for comparison.
How many households does the model predict will switch to fuel wood? Our main
interest here is to predict the number of household that may switch to fuelwood as a
result of electricity price increases. The multinomial regression coefficients do not
directly provide such information, but we can use the model to calculate the change in
predicted probability of each outcome. Table 9 shows the predicted changes in the
proportion of households using electricity only, fuelwood only, or using both electricity
and fuelwood., assuming an increase in electricity prices to 7c/kWh. To assess the
predictive accuracy of the model we first compare the actual proportion of household
using each fuel observed in the data with the multinomial logit model's prediction. As
the first two columns of Table 9 show, the estimated model predicts the proportion of
households using each alternative remarkably well.
19
We use the estimated regression coefficients to predict the change in probability of
each outcome when households face higher electricity prices. This is done holding all
other household characteristics constant and allowing only the relative price of
electricity to vary. We then recalculate the predicted probability of each outcome using
the higher electricity prices. The third column of Table 9 shows the predicted impact of
higher electricity prices on household fuel choice. Overall, the model predicts the
proportion of households using only electricity for heating will fall by 9 percentage
points and proportion of households using fuelwood only will increase by 11
percentage points. This would result in just over a quarter of the population using
electricity only for heating, compared to the baseline of more than a third of the overall
population using electricity only. The proportion of households using fuelwood only
would increase to nearly two thirds of the population. Household dependence on
fuelwood resources, either solely or in conjunction with electricity, would increase to a
nearly three quarters of the population. This could represent a substantial increase in the
quantity of fuelwood consumed and potentially a reason for concern.
Interestingly, the impact of higher electricity prices affects household fuel choice across
all income groups. The proportional impact on the poorest households is largest, and
thus we still observe the proportion of households using electricity for heating
increasing across income groups and the proportion of households using fuelwood
decreasing across income groups. We find that the proportion of households using both
electricity and fuelwood would decrease as result of higher electricity prices. This
happens because some households using both electricity and fuelwood switch to using
fuelwood only for heating. While the proportion of households using both fuels falls in
all income groups, households in the poorest quintile see the largest drop in the use of
both fuels. Households using both electricity and fuelwood would of course adjust the
amount of each fuel consumed and potentially increase the quantity of fuelwood used,
but these trade offs on the quantity of each fuel consumed cannot be captured by the
model estimated.
As a caveat, we must note that the model estimated likely over predicts the likelihood
of households switching to fuelwood for space heating. This is partly because the
model does not account for the capital costs incurred in switching from electricity to
fuelwood. Our model can only consider the cost per unit of energy consumed using the
relative price ratio. Although we use several housing characteristic controls, in some
housing units the option of switching to fuelwood may not be at possible for technical
reasons. Since we do not have data about the feasibility of switching to fuelwood, our
model may over predict the extent switching to fuelwood occurs. Finally, we refrain
from estimating the potential impact of further electricity price increases, since prices
higher than 7 c/kWh would fall far outside the relative price range that we observe in
the current dataset.
20
Table 9. Household fuel choice and electricity prices
Actual fuel choice Estimates of fuel choice by Predicted fuel choice with
proportions observed multinomial logit model higher electricity tariffs
Electric Wood Use Electric Wood Use Electric Wood Use
Quintiles only only both only only both only only both
1 11 75 14 12 78 10 7 87 6
2 22 68 10 20 67 13 12 78 10
3 33 58 9 31 58 11 23 68 8
4 41 43 16 38 46 16 29 58 13
5 60 24 16 59 26 15 49 37 14
ALL 38 49 13 36 51 13 26 63 11
6. Conclusion and policy recommendations
In this note we analyze the impact of higher electricity tariffs on household welfare, the
distributional and fiscal impact of alternative electricity tariff structures, and the
potential impact of higher electricity tariffs on household choice of space heating fuel.
We pay particular attention to the welfare losses of higher electricity tariffs on poor
households and the distributional implications of alternative electricity tariff structures.
Our analysis suggests that the proposed electricity tariff reforms could have a very
significant impact on the welfare of poor households. We find that households in the
poorest quintile spend, on average, twice the share of richest households' expenditures
on energy. The burden of electricity expenditures on households in the poorest quintile
amounts to 10 percent of household expenditures for those relying only on electricity
for heating. As such, the impact of higher electricity prices would affect poor
household's welfare considerably. If we assume that poor households do not switch
heating fuel and have fairly inelastic demand for electricity, they would experience
welfare losses in the range of 3 to 5 percent of household expenditures due to proposed
electricity tariff increases to 7 c/kWh. These impacts, however, depend on the extent
households adjust their consumption of electricity as a result of the higher tariffs and
the type of tariff structure which is adopted.
More than half of the population relies on fuelwood for space heating. Higher
electricity prices could significantly increase the proportion of households using
fuelwood for space heating to nearly two thirds of the population. A higher proportion
of the poor already rely on fuelwood for space heating and more would be expected to
switch to fuelwood as result of higher electricity tariffs. Additional demand for
fuelwood as a result of higher electricity prices therefore could have negative impacts
on fuelwood availability, with increase demand potentially leading to higher fuelwood
prices and an even greater energy affordability problem for poor households. How
electricity price reforms affect household choice of space heating fuel and fuelwood
demand should be carefully monitored. This underscores the importance of keeping in
21
mind the possible unintended consequences of the electricity reforms for the
environment as well as for poverty.
The Government of Montenegro will need to seriously consider the design of a safety
net to protect the vulnerable households and the environment from the full brunt of the
adverse effects of the anticipated pricing reform. Keeping residential electricity prices
at their current level will not be a feasible policy option when the cost-recovery price
begins to rise because of the very high fiscal costs of such a policy. Even without
recourse to public funds in Montenegro, there is significant scope for mitigating the
adverse effect of an electricity tariff increase on the poor households. The poor in
Montenegro consume half as much electricity as the non-poor households, making it
possible to effectively deliver cross-subsidies to the poor by quantity-based targeting
with non-linear pricing schemes.
As we have shown, non-linear tariff schemes may be preferable on the basis of their
targeting and coverage performance compared to linear tariff schemes, although their
implementation may be complicated. In theory, an IBT or a VDT tariff could result in a
much higher share of the total subsidy accruing to the poor than with a linear scheme,
and with an IBT all the poor would be covered by the subsidy. In practice, the
Government of Montenegro would need to consider the implementation issues if a non-
linear scheme were under consideration. The legal issues pose a further obstacle to the
introduction of a non-linear pricing scheme as the Energy Law in Montenegro
stipulates that cross-subsidies to any groups of consumers must be phased out. The
Government of Montenegro could evaluate the possibility of revising the Energy Law,
if it is convinced that the implementation issues will not pose a serious obstacle to
introducing non-linear pricing schemes.
Provision of social assistance through the general social protection program may be
feasible in the long term if a broad-based and well-targeted social protection program is
in place in the future. However, finding the sources of financing for such a program
will be problematic and the Government of Montenegro does not currently plan to
increase social spending on the scale that would be needed to mitigate the energy
reform's welfare impacts. This means that the government, the regulatory agency and
the electric utility company must find innovative ways to finance a social assistance
program for the poor and at the same time enhance energy efficiency in the residential
sector. For example, the stakeholders in the reform process might evaluate the
feasibility of using a portion of the taxes on electricity to create a fund that will finance
projects that will enhance energy efficiency for residential consumers or they might
consider the possibility of designing other programs financed by the private sector.
22
APPENDIX
Table 1. Evaluation of Subsidy Mechanisms
Evaluation No Across IBT Price Burden limit Other Non-
Criteria disconnection the (lifeline) discounts based on earmarked earmarked
board with 2 for actual utility cash cash
price blocks privileged expenditures transfer transfer
subsidy consumers
Coverage 1 1 to 2 1 to 2 1 1 1 1
Targeting 1 0 0 1 0 2 2
Predictability 0 2 2 2 1 1 1
Pricing -2 -2 -1 -1 -1 -1 0
Distortions
Administration 0 0 0 -1 -2 -2 -2
Cost/Difficulty
Aggregate 2 2 to 4 3 to 5 4 0 4 5
Score
Note: Scoring: 0 - low, 1 - medium, 2 - high. Aggregate score was calculated using double weights for the
first two criteria.
Source: The World Bank (2000), "Maintaining Utility Services for the Poor."
23
Table 2. Monthly total average annual household electricity consumption (kWh/month)
deciles of per capita total expenditures
1 2 3 4 5 6 7 8 9 10 Total
North 368 435 507 522 500 641 634 796 865 1151 572
Center 427 576 540 676 706 652 750 754 723 953 654
South 425 519 546 689 630 751 805 690 816 1203 711
Total 401 515 528 625 626 694 730 755 789 1076 642
Source: calculated from 2004 ISSP Montenegro Household Survey by dividing monthly reported electricity
expenditures by the average electricity tariff of 4.85 Eurocents/kWh.
Table 3. Average subsidy (positive) and tax (negative) received or paid by households
(Euro/month)
deciles of total per capita household expenditures
1 2 3 4 5 6 7 8 9 10 Total
Scenario 1 8.8 11.3 12.1 14.1 13.3 15.7 15.3 18.3 17.7 25.5 14.5
Scenario 2 5.8 6.2 6.3 6.4 6.2 6.4 6.4 6.4 6.4 6.4 6.3
Scenario 3 3.5 2.4 1.9 0.5 0.8 -0.7 -0.4 -2.6 -2.1 -8 0
Scenario 4 4.0 2.8 2.0 0.0 0.0 -1.6 -1.5 -2.6 -3.1 -5.0 0
Scenario 5 2.2 1.9 1.0 1.1 0.6 0.9 1.0 1.6 0.7 0.4 1.2
Table 4. Total subsidy (tax) received or paid by households by income category (million Euro/year)
deciles of total per capita household expenditures
1 2 3 4 5 6 7 8 9 10 Total
Total subsidy (tax), million Euro per year
Scenario 1 3.1 3.3 2.5 3.0 2.9 3.3 2.9 3.6 3.2 4.8 32.6
Scenario 2 2.0 1.8 1.3 1.4 1.4 1.3 1.2 1.3 1.2 1.2 14.1
Scenario 3 1.2 0.7 0.4 0.1 0.2 -0.1 -0.1 -0.5 -0.4 -1.5 0
Scenario 4 1.4 0.8 0.4 0.0 0.0 -0.3 -0.3 -0.5 -0.6 -0.9 0
Scenario 5 0.8 0.6 0.2 0.2 0.1 0.2 0.2 0.3 0.1 0.1 2.8
Percent of total subsidy
Scenario 1 9 10 8 9 9 10 9 11 10 15 100
Scenario 2 14 13 9 10 10 10 9 9 8 9 100
Scenario 5 1 20 8 8 4 7 7 12 5 2 100
24